DOCSLIB.ORG

The Surrender Software

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Scientific image rendering forspacesceneswith the SurRender software

Scientific image rendering for space scenes with the SurRender software

R. Brochard, J. Lebreton*, C. Robin, K. Kanani, G. Jonniaux, A. Masson, N. Despré, A. Berjaoui
Airbus Defence andSpace,31rue des Cosmonautes,31402 Toulouse Cedex, France [email protected]
*Corresponding Author

Abstract

The autonomy of spacecrafts can advantageously be enhanced by vision-based navigation (VBN) techniques.
Applications range from manoeuvers around Solar System objects and landing on planetary surfaces, to in-orbit servicing orspace debris removal, and even ground imaging. The development and validation of VBN algorithms for space exploration missions relies on the availability of physically accurate relevant images. Yet archival data from past missions can rarely serve this purpose and acquiring new data is often costly. Airbus has developed the image rendering software SurRender, which addresses the specific challenges of realistic image simulation with high level of representativeness for space scenes. In this paper we introduce the software SurRender and how its unique capabilities have proved successful for a variety of applications. Images are rendered by raytracing, which implements the physical principles of geometrical light propagation. Images are rendered in physical units using a macroscopic instrument model and scene objects reflectance functions. It is specially optimized for space scenes, with huge distances between objects and scenes up to Solar System size. Raytracing conveniently tackles some important effects for VBN algorithms: image quality, eclipses, secondary illumination, subpixel limb imaging, etc. From a userstandpoint, a simulation is easily setup using the available interfaces (MATLAB/Simulink, Python, and more) by specifying the position of the bodies (Sun, planets, satellites, …) over time, complex 3D shapes and material surface properties, before positioning the camera. SurRender comes with its own modelling tool, SuMoL, enabling to go beyond existing models for shapes, materials and sensors (projection, temporal sampling, electronics, etc.). Su r Render is natively designed to simulate different kinds of sensors (visible, LIDAR, …). Additional tools are available for manipulating huge datasets (“giant textures”) used to store albedo maps and digital elevation models (up to 256 TB), or for procedural (fractal) texturing that generates high-quality images for a large range of observing distances (from millions of km to touchdown). We illustrate SurRender performances with a selection of case studies.We place particular emphasis on a Moon landing simulation we recently computed that represents 40 GB of data and a 900-km flyby.The SurRender software canbe made available to interestedreaders uponrequest.

Keywords: computervision,navigation,image rendering,spaceexploration, raytracing Acronyms/Abbreviations

ADS: Airbus Defence and Space removal. It is an asset when manoeuvers do not rely on the ground to avoid transmission delays and also because
BRDF: BidirectionalReflectance DistributionFunction a very high reactivity maximizes the science return of the DEM: Digital Elevation Model GNC: Guidance,Navigation &Control GPS: Global Positioning System GSD: Ground Sample Distance IMU: InertialMeasurementUnit JUICE: JUpiterICy moons Explorer LIDAR: LIght DetectionAndRanging LSB: Least Significant Bit PDS: Planetary Data System PSF: Point Spread Function RAM:RandomAccessMemory R&T: Research andTechnology VBN: Vision-Based Navigation mission during high relative velocity phases. Traditionally, Guidance, Navigation & Control (GNC) filters hybridize the input from several sensors, such as IMUs, GPS (in earth orbit), radars and altimeters. Over the last decades, it has become evident that the addition of measurements from Vision-Based Navigation (VBN) systems greatly improves the performances of autonomous navigation solutions [1, 2, 3]. ADS has developed a portfolio of VBN algorithms including relative navigation, absolute navigation, model-based navigation as well as perception and reconstruction techniques. These computer vision techniques rely on the availability of abundant test images to insure their development and validation.

1. Introduction

Archival data from past missions can be used to test
Solar System exploration probes as well as in-orbit algorithms but, when they are available, they are robotic spacecrafts require a high level of autonomy to generally sensor-specific, they lack exhaustiveness, perform their missions. The applications range from representativeness, and often ground truth references. manoeuvers and landing around Solar System objects, to Some experiments are carried out on ground, thanks to in-orbit robotic servicing missions, or space debris test benches equipped with robotic arms handling sensors

  • V1.0 - October 2, 2018
  • Page 1 of 11

Scientific image rendering forspacesceneswith the SurRender software and target mock-up. Their representativeness is limited in content of a pixel is determined by casting several rays terms of range, spatial irradiance, available scenarios, originating from this pixel, and finding which real world inter alia, and these facilities have high operating costs, objects this ray encounters until it finally intersects with

  • so that onlya fewtest casescanbe run.
  • a light source (Sun/stars, planets or artificial light

To assess performances and robustness of VBN sources). Physical optics-level effects, such as diffraction solutions, computer generated data are highly of the light by the aperture of the imaging instrument, are recommended as they allow testing solutions in taken into account at macroscopic level using a Point exhaustive cases and provide ground truth comparisons. Spread Function (PSF). The light flux is stochastically The objective of the image processing part of the VBN, sampled within the pixels (including the probability derived fromcomputervision technics, is often to extract densityfunctiondefined bythe PSF).

  • information from the image geometry: track features,
  • The raytracer handles multiple diffusions and

match edges, etc. In this context, the capability of the reflections (This recursive raytracing technique is called algorithms to extract such information depends on pathtracing). Interaction of the light with the surface of notions such as texture, contrast, noise, which all come the scene objects is modelled in terms of Bidirectional from a broader field called radiometry. The same Reflectance Distribution Function (BRDF). The objects

  • challengesexist forvision sensordesign.
  • themselves can take arbitrarily complex shapes. The

Some simulator softwares are well-known to the geometry of Solar System objects are described by public: examples include the Celestia and B l ender Digital Elevation Maps (DEM) and spheroid models software or rendering engines used for video game or by which are the basic input needed to calculate light animation studios. Although visually impressive, these scattering.Artificialobjectsare described by3Dmeshes.

  • simulators lack the realism needed for advanced image
  • Providing the incoming irradiance is known, the

processing techniques. Typically they are designed to image is naturally rendered in physical units: each pixel cope with the human vision, but they do not have the contains an irradiance value expressed in W/m². photometric accuracythat actualsensorsare sensitive to. Providing the number of rays is large enough, the realism
Space applications require specialized software. For is ensured at subpixel level. Providing the models are instance the PANGU [4, 5] utility is commonly used in accurate enough, SurRender images are virtually the European space sector. Our team at Airbus has been undistinguishable fromactualphotographs.

  • developing since 2011 the image rendering software
  • In its standard implementation SurRender produces

SurRender, which addresses the specific challenges of visible (scattered light) images (although it is potentially physical image simulation (raytracing) with high level of able to make thermal infrared images, see Sec. 6.1). In representativeness for space scenes. Even if it is based on addition to images, useful output is generated such as classical 3D rendering techniques, it adapts those to depth maps (range imaging) and normal maps (slope specific space properties: high variability of objects mapping,hazard detection and avoidance). Active optical sizes, very high range between them, specific optical sensors such as LiDARs can also be modelled with properties. The software recently went through a formal SurRender (Sec. 2.4). qualification process, it has been used in many R&D and technological development projects for ESA (JUICE, GENEVIS), CNES, for the European Community

Sun

(NEOShield-2) and internalprojects(e.g.SpaceTug).
Section 2 of this paper presents the methods that

Light source

sampling

compose the Su r Render software, fromfirst principles,to computational implementation. In section 3, we introduce the user interfaces, the embedded modelling language SuMoL and sensor models. Section 4 presents a variety of tests that have been made for its formal validation. In Section 5 we demonstrate the performances of SurRender by focusing on the example of a Moon landing application. In section 6 we list additional examples of applications. Finally in Section 7 we discuss future ambitionsand collaboration opportunities.

Object 1

Optics
Image

plane

(sensor)

Object 2

Light bounces

Fig. 1: Principleofbackward raytracing 2.2 Compute r i mplementation

SurRender enables two distinct modes of image rendering: raytracing as presented above and OpenGL (Open Graphics Library). The latter runs on GPU and allows very fast image generation, compatible with realtime processing, but at the cost of physical accuracy and

2. Methods

2.1 Physical principles

SurRender implements the physical principles of light propagation (see Fig. 1). It solves geometrical optics

equations with (backward-) r a ytracing techniques. The

  • V1.0 - October 2, 2018
  • Page 2 of 11

Scientific image rendering forspacesceneswith the SurRender software fine optics/sensor modelling. This rendering mode is thus 2.3 Optimizations suitable for real-time setups, which do not focus on fine The implementation of raytracing techniques in the functional performance. In this paper we focus on the context of space exploration poses several challenges. raytracing model. SurRender generates images using backward raytracing.
The rendering engine is written in C++, it runs on Raytracing is a stochastic process, in which rays are
CPU and on multiple cores. It is currently in its version casted recursively from pixel to light source. In order to 5. The raytracer is implemented using classical reduce the noise intrinsic to this stochastic process, a acceleration structures (Kd-tree, Bounding Volume great number of rays must be traced. This process is Hierarchy) optimized for sparse scenes, as well as relief computationally intensive, and the raytracing software mapping with more specific ray-marching techniques [6, must be heavily optimized to ensure the maximum image 7]). In addition to a number of optimizations detailed in quality (lowest noise) within a minimum rendering time. Section 2.3, SurRender is characterized by a highly Space scenes are generally sparse. Randomly sampling

  • efficient management ofcache memory.
  • the volume space would therefore prove highly

To that purpose specific data formats were developed inefficient since most rays back-propagated from the that are specially designed for space applications. A first detector pixels would not intercept any object. This pre-processing is applied to DEM to calculate cone maps  difficulty is addressed by explicitly targeting the rays and h e ight maps. Cone maps represents, for each given toward scene objects, thus avoiding a lot of unnecessary point, the cone in which a ray can be traced without computation. The ray casting is focused in directions that encountering an obstacle. Height maps represent the contribute to the final image. Small objects are rendered height with respect to a reference elevation. So-called efficiently by explicitly targeting them at a su b -pixel giant textures are used to store them. Giant textures are level. This allows simulating continuously a rendezvous memory-mapped, and loaded in RAM on demand: a mission from infinity to contact. SurRender also pyramid scheme is used in such a way that only the level implements forward raytracing functionalities, enabling of details needed (given the size and resolution at stake photon mapping, a process that provides maps of for a given scene) is loaded. The textures can be received surface radiances forall objects in the scene. All generated from actual space mission data using common these optimizations – among others - enable to simulate formats (PNG, JPEG, etc.) or from PDS data*. They can scenesat SolarSystemscales.

also be enriched using a procedural fractal generator to

create additional details at small scales. This data 3. How to use SurRender

management scheme is up to 50 times more efficient than 3.1 Use r i nterfaces

  • manipulating meshesforplanetarysurfaces.
  • The main application runs a s e rver that can be located

Computations are generally performed with double on the same computer as the client part, or on a remote precision, which is essential for planet size objects. computer. Users interact with the server using the Indeed the elevation variations between the typical surrender client. The server receives commands fromthe surface elevation of a Solar System object and its radius client trough a TCP/IP link, and sends the resulting is about the order of magnitude of the LSB of floating image back. If sent over a network, images can be point numbers (typically 1 m vs 1000 km). However compressed to reduce the transmission duration. A height maps and cone maps can be stored respectively in specialized mode exists that emulates a camera by

compactfloat and hal f f loa tformats forefficiency†.

redirecting the data fluxtowards an optical stimulator for
The size of the giant textures can far exceed the use with real sensors on test benches (hardware-in-the- available RAM/cache memory available on the loop)orto anotherclient like an Ethernet camera. computer. To that purpose SurRender can be used with the FUSE software to create arbitrarily large virtual files scripts. Interfaces are available in a variety of language: to manage the fractaltextures. MATLAB, Simulink, Python 3, C++, Lua, Java. Cloud
For the user, SurRender is instantiated using external
Generating an image using raytracing lasts from a few computing capabilities were recently implemented seconds to several minutes depending on the desired enabling a huge gain in performances through a higher

  • image quality and the scene content.
  • levelof parallelization.

The scripts describe the scene and its objects: camera, light sources, stars, planets, satellites, their positions, attitudes, shapes and surface properties. There is no intrinsic limitation in the complexity of the scene within this framework. A number of default models are provided that can easily be modified, parameterized or complemented by the user using the SuMoL language (see Sec. 3.2).

*

"PlanetaryData System", https://pds.nasa.gov. Floats are stored on 32 bits, doubles on 64 bits. Half floats

†

and compact floats (Su r R e n der-specific) are encoded only on 16

bits (reduced precision), respectively 17 bits (almost as precise as 32bits floats).

  • V1.0 - October 2, 2018
  • Page 3 of 11

Scientific image rendering forspacesceneswith the SurRender software
External tools are provided in the package in order to global shutter), HAS2 and HAS3 sensors [11] - which prepare the datasets (formatting, and pre-calculation of implement windowing and rolling shutter (see Fig. 2) - cone maps, fractal generator, etc.). Using these tools, it is and more. very easy to import data at usual formats such as the NASA PDSformat.
Active optical sensors such as LIDARs or time-offlight cameras can also be used. The simulation includes the light emission (and reflections) from the spot or the laser.

3.2 SuMoL

SurRender embeds its own language, SuMoL
(standing for SurRender Modelling Language), a highlevel programming language derived from C. SuMoL is used to model various components of a simulation. The list of currently available models we provide includes:
- Projection models (pinhole,fisheye,orthographic) - Geometrical objects (spheres, spheroids, planar & sphericalDEMs)
- BRDFs (Lambertian / mate, Mirror, Hapke [8], Phong [9],Oren-Nayar[10])
- PSFs (can be field-dependent andchromatic) - Sampling (sequential,rolling shutter, snapshot,etc.) - Sensors properties (Section2.5).
They implement analytical (or numerical) models, and

they can be interfaced with the client “on-the-fly” i.e.

without recompiling the whole software. They make SurRender a very versatile rendering tool, as new or specific sensor and material behaviors can be easily described by the user without changing the rendering engine. A SuMoL editor is provided as an external tool so that a user can easily implement his/her own models if needed.

Fig. 2 : HAS3 sensor simulation. Sensor sub - w indows at different integration times enable si m u ltaneous observatio n of f aint sta r s a ndbright object s .

4. Validation and performances

4.1 Analytica l val i dation

SurRender has passed a formal qualification review internal to Airbus. Its physical accuracy has been tested in specific contexts that can be studied analytically, allowing quantitative assessments. Both geometric and radiometric correctness have been controlled. We verified the (subpixel) position, orientation and irradiance of objects (points, spheroids, textures) in the image plane (with pinhole model, distortions, rolling shutter), including BRDFs (Lambertian sphere or plane) and the PSF effect. The tests demonstrated exquisite accuracy and precision. The report of this analysis can be provided toan interestedreaderupon request.

3.3 Senso r m odels

By default “ideal” images are produced, not altered

by any real life optical effects, in irradiance units (W/m²)‡. The images can be retrieved in greyscale or in multiple wavelengths channels: the bandwidth is specified by the user, and the BRDF and stellar spectra are chromatic. These images already account for the sensor field of view (FOV) and the size of the pixel matrix. A PSF can be specified in order to emulate diffraction by the optics.
Importantly fine sensor models can be implemented using external scripts and SuMoL wrappers. Various sensor properties and setups can be tuned: integration time, readout noise, photon noise, transmission, gain, pupil diameter, motion blur, defocus, etc. The acquisition mode can also be simulated: rolling or global shutter, push-broom, snapshot, windowing (accounting for detector motion during the acquisition). Recently the possibility of a PSF varying in the field-of-view was implemented, as well as chromatic aberrations. All these effects are natural byproducts of the raytracer. Available

sensors include a “generic sensor” (classical noises and

This analytical validation was completed by a qualitative study of rendered images for complex scenes against realimages.

4.2 Validation wit h r eal scenes

We conducted a classical test in computer graphics:

the accuracy of the “Cornell box” rendering. Fig. 3

allows qualitative comparisons of SurRender raytracing results, with real data photographed with a CCD camera. The visual properties of the image are correctly simulated, with various optical effects such as shadows, BRDFs, inter-reflections, etc., even though in this test, pieces of information were missing (PSF, postprocessing, precise camera pose, light spectrum). This particular simulation is very demanding due to the nonsparsity of the scene and the multiple reflections involved.

Recommended publications
  • Report Resumes

    Report Resumes

    REPORT RESUMES ED 019 218 88 SE 004 494 A RESOURCE BOOK OF AEROSPACE ACTIVITIES, K-6. LINCOLN PUBLIC SCHOOLS, NEBR. PUB DATE 67 EDRS PRICEMF.41.00 HC-S10.48 260P. DESCRIPTORS- *ELEMENTARY SCHOOL SCIENCE, *PHYSICAL SCIENCES, *TEACHING GUIDES, *SECONDARY SCHOOL SCIENCE, *SCIENCE ACTIVITIES, ASTRONOMY, BIOGRAPHIES, BIBLIOGRAPHIES, FILMS, FILMSTRIPS, FIELD TRIPS, SCIENCE HISTORY, VOCABULARY, THIS RESOURCE BOOK OF ACTIVITIES WAS WRITTEN FOR TEACHERS OF GRADES K-6, TO HELP THEM INTEGRATE AEROSPACE SCIENCE WITH THE REGULAR LEARNING EXPERIENCES OF THE CLASSROOM. SUGGESTIONS ARE MADE FOR INTRODUCING AEROSPACE CONCEPTS INTO THE VARIOUS SUBJECT FIELDS SUCH AS LANGUAGE ARTS, MATHEMATICS, PHYSICAL EDUCATION, SOCIAL STUDIES, AND OTHERS. SUBJECT CATEGORIES ARE (1) DEVELOPMENT OF FLIGHT, (2) PIONEERS OF THE AIR (BIOGRAPHY),(3) ARTIFICIAL SATELLITES AND SPACE PROBES,(4) MANNED SPACE FLIGHT,(5) THE VASTNESS OF SPACE, AND (6) FUTURE SPACE VENTURES. SUGGESTIONS ARE MADE THROUGHOUT FOR USING THE MATERIAL AND THEMES FOR DEVELOPING INTEREST IN THE REGULAR LEARNING EXPERIENCES BY INVOLVING STUDENTS IN AEROSPACE ACTIVITIES. INCLUDED ARE LISTS OF SOURCES OF INFORMATION SUCH AS (1) BOOKS,(2) PAMPHLETS, (3) FILMS,(4) FILMSTRIPS,(5) MAGAZINE ARTICLES,(6) CHARTS, AND (7) MODELS. GRADE LEVEL APPROPRIATENESS OF THESE MATERIALSIS INDICATED. (DH) 4:14.1,-) 1783 1490 ,r- 6e tt*.___.Vhf 1842 1869 LINCOLN PUBLICSCHOOLS A RESOURCEBOOK OF AEROSPACEACTIVITIES U.S. DEPARTMENT OF HEALTH, EDUCATION & WELFARE OFFICE OF EDUCATION K-6) THIS DOCUMENT HAS BEEN REPRODUCED EXACTLY AS RECEIVED FROM THE PERSON OR ORGANIZATION ORIGINATING IT.POINTS OF VIEW OR OPINIONS STATED DO NOT NECESSARILY REPRESENT OFFICIAL OFFICE OF EDUCATION POSITION OR POLICY. 1919 O O Vj A PROJECT FUNDED UNDER TITLE HIELEMENTARY AND SECONDARY EDUCATION ACT A RESOURCE BOOK OF AEROSPACE ACTIVITIES (K-6) The work presentedor reported herein was performed pursuant to a Grant from the U.
  • Monte Carlo Methods to Calculate Impact Probabilities⋆

    Monte Carlo Methods to Calculate Impact Probabilities⋆

    A&A 569, A47 (2014) Astronomy DOI: 10.1051/0004-6361/201423966 & c ESO 2014 Astrophysics Monte Carlo methods to calculate impact probabilities? H. Rickman1;2, T. Wisniowski´ 1, P. Wajer1, R. Gabryszewski1, and G. B. Valsecchi3;4 1 P.A.S. Space Research Center, Bartycka 18A, 00-716 Warszawa, Poland e-mail: [email protected] 2 Dept. of Physics and Astronomy, Uppsala University, Box 516, 75120 Uppsala, Sweden 3 IAPS-INAF, via Fosso del Cavaliere 100, 00133 Roma, Italy 4 IFAC-CNR, via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy Received 9 April 2014 / Accepted 28 June 2014 ABSTRACT Context. Unraveling the events that took place in the solar system during the period known as the late heavy bombardment requires the interpretation of the cratered surfaces of the Moon and terrestrial planets. This, in turn, requires good estimates of the statistical impact probabilities for different source populations of projectiles, a subject that has received relatively little attention, since the works of Öpik(1951, Proc. R. Irish Acad. Sect. A, 54, 165) and Wetherill(1967, J. Geophys. Res., 72, 2429). Aims. We aim to work around the limitations of the Öpik and Wetherill formulae, which are caused by singularities due to zero denominators under special circumstances. Using modern computers, it is possible to make good estimates of impact probabilities by means of Monte Carlo simulations, and in this work, we explore the available options. Methods. We describe three basic methods to derive the average impact probability for a projectile with a given semi-major axis, eccentricity, and inclination with respect to a target planet on an elliptic orbit.
  • 1 Practical Rendezvous Scenario For

    1 Practical Rendezvous Scenario For

    PRACTICAL RENDEZVOUS SCENARIO FOR TRANSPORTATION MISSIONS TO CIS-LUNAR STATION IN EARTH–MOON L2 HALO ORBIT Naomi Murakami(1), Satoshi Ueda(1), Toshinori Ikenaga(1), Maki Maeda(1), Toru Yamamoto(1), and Hitoshi Ikeda(1) (1)Japan Aerospace Exploration Agency, 2-1-1 Sengen, Tsukuba, Ibaraki, 305-8505, Japan (1)+81-50-3362-3725, [email protected] Abstract: Plans to establish a new international space station in the vicinity of the Moon have drawn attention as a potential gateway for future missions. Assuming that such a station will be constructed, various rendezvous missions will be performed, including logistics flight and crew transportation missions. In the low Earth orbit (LEO), many logistics flight missions have been performed, such as the H-II Transfer Vehicle (HTV) and the Automated Transfer Vehicle (ATV) missions for the International Space Station (ISS), and rendezvous operation technology has been well established. However, in cis-lunar orbits, the dynamics of relative motion are different from those in LEOs, and the GPS- based navigation scheme, which is the key navigation method in LEO rendezvous missions, cannot be applied. Hence, a different type of rendezvous scheme is necessary for application to rendezvous missions in cis-lunar orbits. In this study, we suggest a practical rendezvous scenario for logistics flight missions to the future cis-lunar space station in the Earth–Moon L2 halo orbit and clarify the problems and requirements regarding guidance, navigation, and control (GN&C) for cis-lunar rendezvous. Keywords: Rendezvous, Cis-lunar station, Earth–Moon L2 halo, GN&C. 1. Introduction The updated Global Exploration Roadmap delivered by the International Space Exploration Coordination Group (ISECG) outlines strategies for human and robotic exploration to the Moon, asteroids, and Mars [1].
  • Project Selene: AIAA Lunar Base Camp

    Project Selene: AIAA Lunar Base Camp

    Project Selene: AIAA Lunar Base Camp AIAA Space Mission System 2019-2020 Virginia Tech Aerospace Engineering Faculty Advisor : Dr. Kevin Shinpaugh Team Members : Olivia Arthur, Bobby Aselford, Michel Becker, Patrick Crandall, Heidi Engebreth, Maedini Jayaprakash, Logan Lark, Nico Ortiz, Matthew Pieczynski, Brendan Ventura Member AIAA Number Member AIAA Number And Signature And Signature Faculty Advisor 25807 Dr. Kevin Shinpaugh Brendan Ventura 1109196 Matthew Pieczynski 936900 Team Lead/Operations Logan Lark 902106 Heidi Engebreth 1109232 Structures & Environment Patrick Crandall 1109193 Olivia Arthur 999589 Power & Thermal Maedini Jayaprakash 1085663 Robert Aselford 1109195 CCDH/Operations Michel Becker 1109194 Nico Ortiz 1109533 Attitude, Trajectory, Orbits and Launch Vehicles Contents 1 Symbols and Acronyms 8 2 Executive Summary 9 3 Preface and Introduction 13 3.1 Project Management . 13 3.2 Problem Definition . 14 3.2.1 Background and Motivation . 14 3.2.2 RFP and Description . 14 3.2.3 Project Scope . 15 3.2.4 Disciplines . 15 3.2.5 Societal Sectors . 15 3.2.6 Assumptions . 16 3.2.7 Relevant Capital and Resources . 16 4 Value System Design 17 4.1 Introduction . 17 4.2 Analytical Hierarchical Process . 17 4.2.1 Longevity . 18 4.2.2 Expandability . 19 4.2.3 Scientific Return . 19 4.2.4 Risk . 20 4.2.5 Cost . 21 5 Initial Concept of Operations 21 5.1 Orbital Analysis . 22 5.2 Launch Vehicles . 22 6 Habitat Location 25 6.1 Introduction . 25 6.2 Region Selection . 25 6.3 Locations of Interest . 26 6.4 Eliminated Locations . 26 6.5 Remaining Locations . 27 6.6 Chosen Location .
  • Can Moons Have Moons?

    Can Moons Have Moons?

    A MNRAS 000, 1–?? (2018) Preprint 23 January 2019 Compiled using MNRAS L TEX style file v3.0 Can Moons Have Moons? Juna A. Kollmeier1⋆ & Sean N. Raymond2† 1 Observatories of the Carnegie Institution of Washington, 813 Santa Barbara St., Pasadena, CA 91101 2 Laboratoire d’Astrophysique de Bordeaux, Univ. Bordeaux, CNRS, B18N, all´eGeoffroy Saint-Hilaire, 33615 Pessac, France Accepted XXX. Received YYY; in original form ZZZ ABSTRACT Each of the giant planets within the Solar System has large moons but none of these moons have their own moons (which we call submoons). By analogy with studies of moons around short-period exoplanets, we investigate the tidal-dynamical stability of submoons. We find that 10 km-scale submoons can only survive around large (1000 km-scale) moons on wide-separation orbits. Tidal dissipation destabilizes the orbits of submoons around moons that are small or too close to their host planet; this is the case for most of the Solar System’s moons. A handful of known moons are, however, capable of hosting long-lived submoons: Saturn’s moons Titan and Iapetus, Jupiter’s moon Callisto, and Earth’s Moon. Based on its inferred mass and orbital separation, the newly-discovered exomoon candidate Kepler-1625b-I can in principle host a large submoon, although its stability depends on a number of unknown parameters. We discuss the possible habitability of submoons and the potential for subsubmoons. The existence, or lack thereof, of submoons, may yield important constraints on satellite formation and evolution in planetary systems. Key words: planets and satellites – exoplanets – tides 1 INTRODUCTION the planet spins quickly or to shrink if the planet spins slowly (e.g.
  • Planning a Mission to the Lunar South Pole

    Planning a Mission to the Lunar South Pole

    Lunar Reconnaissance Orbiter: (Diviner) Audience Planning a Mission to Grades 9-10 the Lunar South Pole Time Recommended 1-2 hours AAAS STANDARDS Learning Objectives: • 12A/H1: Exhibit traits such as curiosity, honesty, open- • Learn about recent discoveries in lunar science. ness, and skepticism when making investigations, and value those traits in others. • Deduce information from various sources of scientific data. • 12E/H4: Insist that the key assumptions and reasoning in • Use critical thinking to compare and evaluate different datasets. any argument—whether one’s own or that of others—be • Participate in team-based decision-making. made explicit; analyze the arguments for flawed assump- • Use logical arguments and supporting information to justify decisions. tions, flawed reasoning, or both; and be critical of the claims if any flaws in the argument are found. • 4A/H3: Increasingly sophisticated technology is used Preparation: to learn about the universe. Visual, radio, and X-ray See teacher procedure for any details. telescopes collect information from across the entire spectrum of electromagnetic waves; computers handle Background Information: data and complicated computations to interpret them; space probes send back data and materials from The Moon’s surface thermal environment is among the most extreme of any remote parts of the solar system; and accelerators give planetary body in the solar system. With no atmosphere to store heat or filter subatomic particles energies that simulate conditions in the Sun’s radiation, midday temperatures on the Moon’s surface can reach the stars and in the early history of the universe before 127°C (hotter than boiling water) whereas at night they can fall as low as stars formed.
  • Lunar Station Protection: Lunar Regolith Shielding

    Lunar Station Protection: Lunar Regolith Shielding

    Lunar Station Protection: Lunar Regolith Shielding By Nancy J. Lindsey, LMTO Suite 200, 7474 Greenway Center Drive, Greenbelt, MD 20770 International Lunar Conference 2003 Session 5: Science Of, From and On the Moon: Life Sciences and Habitation Hawaii Island, Hawaii ABSTRACT The Moon’s environment consists of a combination of atmospheric, thermal, meteoroids, radiation, magnetic field, and gravitational field mechanisms. However, shielding can only be used to protect a lunar station and its inhabitants from the effects of the thermal, radiation, and meteoroid mechanisms. This paper provides an evaluation the effectiveness of the in-situ resource, lunar regolith, to mitigate the effects of the lunar environment on lunar station and its inhabitants when used as a shield. It includes a lunar environmental human life threat assessment, calculates regolith required for crew protection, and provides a regolith usage viability summary showing how lunar regolith should be viewed as a viable and effective in-situ life support system resource today, due to its shielding properties, and in the future, due to its O2 generating and heat storage potential as well as its shielding properties. BACKGROUND Lunar Environmental Details The Moon’s environment consists of a combination of atmospheric, thermal, meteoroids, radiation, magnetic field, and gravitational field mechanisms. However, lunar regolith can only be used to shield a lunar station and its inhabitants from the effects of the thermal, radiation, and meteoroid mechanisms. Therefore only the magnitude of each of these environmental mechanisms on the Moon is detailed in the following sections, thereby establishing a reference environment for human risk and regolith protection effectiveness determination.
  • Calendrical Calculations: the Millennium Edition Edward M

    Calendrical Calculations: the Millennium Edition Edward M

    Errata and Notes for Calendrical Calculations: The Millennium Edition Edward M. Reingold and Nachum Dershowitz Cambridge University Press, 2001 4:45pm, December 7, 2006 Do I contradict myself ? Very well then I contradict myself. (I am large, I contain multitudes.) —Walt Whitman: Song of Myself All those complaints that they mutter about. are on account of many places I have corrected. The Creator knows that in most cases I was misled by following. others whom I will spare the embarrassment of mention. But even were I at fault, I do not claim that I reached my ultimate perfection from the outset, nor that I never erred. Just the opposite, I always retract anything the contrary of which becomes clear to me, whether in my writings or my nature. —Maimonides: Letter to his student Joseph ben Yehuda (circa 1190), Iggerot HaRambam, I. Shilat, Maaliyot, Maaleh Adumim, 1987, volume 1, page 295 [in Judeo-Arabic] If you find errors not given below or can suggest improvements to the book, please send us the details (email to [email protected] or hard copy to Edward M. Reingold, Department of Computer Science, Illinois Institute of Technology, 10 West 31st Street, Suite 236, Chicago, IL 60616-3729 U.S.A.). If you have occasion to refer to errors below in corresponding with the authors, please refer to the item by page and line numbers in the book, not by item number. Unless otherwise indicated, line counts used in describing the errata are positive counting down from the first line of text on the page, excluding the header, and negative counting up from the last line of text on the page including footnote lines.
  • Constraints on the Habitability of Extrasolar Moons 3 ¯Glob Its Orbit-Averaged Global Energy flux Fs

    Constraints on the Habitability of Extrasolar Moons 3 ¯Glob Its Orbit-Averaged Global Energy flux Fs

    Formation, detection, and characterization of extrasolar habitable planets Proceedings IAU Symposium No. 293, 2012 c 2012 International Astronomical Union Nader Haghighipour DOI: 00.0000/X000000000000000X Constraints on the habitability of extrasolar moons Ren´eHeller1 and Rory Barnes2,3 1Leibniz Institute for Astrophysics Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam email: [email protected] 2University of Washington, Dept. of Astronomy, Seattle, WA 98195, USA 3Virtual Planetary Laboratory, NASA, USA email: [email protected] Abstract. Detections of massive extrasolar moons are shown feasible with the Kepler space telescope. Kepler’s findings of about 50 exoplanets in the stellar habitable zone naturally make us wonder about the habitability of their hypothetical moons. Illumination from the planet, eclipses, tidal heating, and tidal locking distinguish remote characterization of exomoons from that of exoplanets. We show how evaluation of an exomoon’s habitability is possible based on the parameters accessible by current and near-future technology. Keywords. celestial mechanics – planets and satellites: general – astrobiology – eclipses 1. Introduction The possible discovery of inhabited exoplanets has motivated considerable efforts towards estimating planetary habitability. Effects of stellar radiation (Kasting et al. 1993; Selsis et al. 2007), planetary spin (Williams & Kasting 1997; Spiegel et al. 2009), tidal evolution (Jackson et al. 2008; Barnes et al. 2009; Heller et al. 2011), and composition (Raymond et al. 2006; Bond et al. 2010) have been studied. Meanwhile, Kepler’s high precision has opened the possibility of detecting extrasolar moons (Kipping et al. 2009; Tusnski & Valio 2011) and the first dedicated searches for moons in the Kepler data are underway (Kipping et al.
  • Icebreaker: a Lunar South Pole Exploring Robot Cmu-Ri-Tr-97-22

    Icebreaker: a Lunar South Pole Exploring Robot Cmu-Ri-Tr-97-22

    ICEBREAKER: A LUNAR SOUTH POLE EXPLORING ROBOT CMU-RI-TR-97-22 Matthew C. Deans Alex D. Foessel Gregory A. Fries Diana LaBelle N. Keith Lay Stewart Moorehead Ben Shamah Kimberly J. Shillcutt Professor: Dr. William Whittaker The Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 Spring 1996-97 Executive Summary Icebreaker: A Lunar South Pole Exploring Robot Due to the low angles of sunlight at the lunar poles, craters and other depressions in the polar regions can contain areas which are in permanent darkness and are at cryogenic temperatures. Many scientists have theorized that these cold traps could contain large quantities of frozen volatiles such as water and carbon dioxide which have been deposited over billions of years by comets, meteors and solar wind. Recent bistatic radar data from the Clementine mission has yielded results consistent with water ice at the South Pole of the Moon however Earth based observations from the Arecibo Radar Observatory indicate that ice may not exist. Due to the controversy surrounding orbital and Earth based observations, the only way to definitively answer the question of whether ice exists on the Lunar South Pole is in situ analysis. The discovery of water ice and other volatiles on the Moon has many important benefits. First, this would provide a source of rocket fuel which could be used to power rockets to Earth, Mars or beyond, avoiding the high cost of Earth based launches. Secondly, water and carbon dioxide along with nitrogen from ammonia form the essential elements for life and could be used to help support human colonies on the Moon.
  • The Peaks of Eternal Light

    The Peaks of Eternal Light

    The Peaks of Eternal Light: a Near-term Property Issue on the Moon Martin Elvis a,1, Tony Milligan b, Alanna Krolikowskic a Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge MA 02138, USA; [email protected]; b Department of Theology and Religious Studies, King’s College London, Virginia Wolf Building, 22 Kingsway, London WC2B 6NR. c Georg-August Universität Göttingen, Heinrich-Düker-Weg 14, 37073, Göttingen, Germany; [email protected] ABSTRACT The Outer Space Treaty makes it clear that the Moon is the ‘province of all mankind’, with the latter ordinarily understood to exclude state or private appropriation of any portion of its surface. However, there are indeterminacies in the Treaty and in space law generally over the issue of appropriation. These indeterminacies might permit a close approximation to a property claim or some manner of ‘quasi-property’. The recently revealed highly inhomogeneous distribution of lunar resources changes the context of these issues. We illustrate this altered situation by considering the Peaks of Eternal Light. They occupy about one square kilometer of the lunar surface. We consider a thought experiment in which a Solar telescope is placed on one of the Peaks of Eternal Light at the lunar South pole for scientific research. Its operation would require non- disturbance, and hence that the Peak remain unvisited by others, effectively establishing a claim of protective exclusion and de facto appropriation. Such a telescope would be relatively easy to emplace with today’s technology and so poses a near-term property issue on the Moon. While effective appropriation of a Peak might proceed without raising some of the familiar problems associated with commercial development (especially lunar mining), the possibility of such appropriation nonetheless raises some significant issues concerning justice and the safeguarding of scientific practice on the lunar surface.
  • A Physically-Based Night Sky Model Henrik Wann Jensen1 Fredo´ Durand2 Michael M

    A Physically-Based Night Sky Model Henrik Wann Jensen1 Fredo´ Durand2 Michael M

    To appear in the SIGGRAPH conference proceedings A Physically-Based Night Sky Model Henrik Wann Jensen1 Fredo´ Durand2 Michael M. Stark3 Simon Premozeˇ 3 Julie Dorsey2 Peter Shirley3 1Stanford University 2Massachusetts Institute of Technology 3University of Utah Abstract 1 Introduction This paper presents a physically-based model of the night sky for In this paper, we present a physically-based model of the night sky realistic image synthesis. We model both the direct appearance for image synthesis, and demonstrate it in the context of a Monte of the night sky and the illumination coming from the Moon, the Carlo ray tracer. Our model includes the appearance and illumi- stars, the zodiacal light, and the atmosphere. To accurately predict nation of all significant sources of natural light in the night sky, the appearance of night scenes we use physically-based astronomi- except for rare or unpredictable phenomena such as aurora, comets, cal data, both for position and radiometry. The Moon is simulated and novas. as a geometric model illuminated by the Sun, using recently mea- The ability to render accurately the appearance of and illumi- sured elevation and albedo maps, as well as a specialized BRDF. nation from the night sky has a wide range of existing and poten- For visible stars, we include the position, magnitude, and temper- tial applications, including film, planetarium shows, drive and flight ature of the star, while for the Milky Way and other nebulae we simulators, and games. In addition, the night sky as a natural phe- use a processed photograph. Zodiacal light due to scattering in the nomenon of substantial visual interest is worthy of study simply for dust covering the solar system, galactic light, and airglow due to its intrinsic beauty.